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Digital Enterprise Research Institute,National University of Ireland, Galway,

Galway, Irelandfirstname.lastname@deri.org

Abstract. In this pap er, we target a context-awareness approach to sensors,prop osing aﬁrst extension of sensor ontologies in this direction. Our proposal aims at emulating the human cognitive ability, taking advantage of LinkedData, in order to improve the human understanding of reality through sensors.

quite diﬀerent from a scientiﬁc one. This problem was also identiﬁed by [7] whoprop ose a division of the quality space into Scientiﬁc and Cognitive ones. Severalother ontologies fo cus on other asp ect, e.g., the MMI Device [10] and CSIRO [3]ontologies fo cus on system and capabilities, and pro cess comp osition, while [6]addresses sensor self-discovery, self-description and classiﬁcation of devices.

2 A Contextualised Cognitive Persp ective

One goal of sensors is to extend human awareness ab out reality. Hence, a way tosatisfy human exp ectations ab out sensor data representation andﬁltering is toemulate the human way of representing andﬁltering this data. Humans canunderstand an event b etter when it can b e asso ciated with a similar past experience stored in memory [2]. We try to use the same mechanism to let a sensorunderstand an event. This will improve/enable its understanding of what is happening around it (reality ) and of what it is actually sensing (selfawareness ).

Technologically, sensors can emulate these human cognitive and asso ciativemechanism by searching for similarevents from the past, using the Linking Op enData (LOD) cloud [1]. As opp osed to the human memory,the “memory” of the LODcloud is virtually unlimited and a sensor acting like a human would b e p otentiallyable to understand what is hidden b ehind raw data, b etter than humans could do.This view provides the dual approach to the human acting like a sensor as proposed by [5] and further investigated by [9].

where we would need to know the amount of water we should provide to aparticular plant to supp ort its healthy growth.

Then a search engine should b e able to retrieve all the sensors that aresensing daily feeding and growth data of that particular plant. This is p ossible onlyif sensors themselves exp ose information ab out what they are sensing. Thequestion is: how can they understand automatically what they are sensing?Sensors could compare their data features to other similar ones, that are stored inthe LOD cloud and have b een already asso ciated with their corresp onding realevent. Searching for similar data to link represent exactly the application of theLinked Data paradigm-

Indeed the whole pro cess of reasoning over the similardata found to infere what the sensor has currently sensed, corresp onds to anemulation of the human cognitive approach, with which the same task is sharedthat is a b etter understanding of reality.

In this example, the LOD Cloud corresp onds to human memory. Yet, whileintothe human memory, some past exp eriences could have b een removed or modiﬁed, LOD is virtually unlimited and data is not sub ject to “corrosion over time”—

Likeliness to b e integrated by other domain-sp eciﬁc external ontologies, andsubsequently to make the integration pro cess easier;

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Community within W3C, and p ossible further opp ortunities with W3C in termsof standardisation.

An ontology to describ e events and their relations As the event description mo del,we cho ose the Event Mo del F [8] for the follwoing reasons:

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it allows us to describ e relations among events, i.e., Correlation, Causality,Mereologic and Interpretation (see Fig. 2), in the most detailed way, as discussedin [8];

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it relies exclusively on

ontology design pattern;–

it isaligned with the DOLCE+DnS Ultralite ontology.

An upp er-level ontology We consider the description of sensor context to b ecritical for sensor discovery. To address this issue the Description and Situation(DnS) ontology

is a very useful to ol as it allows us to describ e situations takinginto account which entities are involved, their role, and the algorithm that theymust satisfy with resp ect to the involving situation. This is also why we choseDOLCE+DnS Ultralite [4] as an upp er ontology. On the one hand, b oth SSNXGand Event F ontologies were already aligned with it; on the other hand, it do esnot contain high-level concepts that are unlikely to b e linked directly such as perdurants and endurants.4 Myriam Leggieri, Alexandre Passant, and Manfred Hauswirth

Our prop osal In order to show how DnS concepts can b e useful and applied into thesensor and sensor network domain, we created the concepts of SensorHierarchy,SensorProjectRole and SensorRole. They are all sub-concepts of classes fromDOLCE+DnS Ultralite (DUL). In particular they share the least common ancestorsSocialObject , Object and Event . The rationale for these concepts is as follows:

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SensorHierarchy

(see Fig. 1) is added as a sub-concept of Design, Description .We think that a description of the network top ology (to automatically annotate it)could help in understanding the sensor data application domain and inferring moredetails over the sp eciﬁc

lo cation of the sensor into the environment. For example, ifa sensor is part of a network fo cused on o ceanographic monitoring, it is probably located under water.

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SensorProjectRole (see Fig. 2) is intro duced as a sub-concept of PlanExecution,Situation. This can work as a bridge b etween our ontology and pro ject or sensor project domain sp eciﬁc external ontologies. The aim of this description is to provide anadditional reﬁnement over the p otential domain of the particular sensor data collected

by a sensor.

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SensorRole (see Fig. 2) is a sub-concept of Role . The motivation follows theapproach of the SensorProjectRole one: To provide a set of concepts relevant fora sensor with resp ect to the pro jects in which it is involved in and its own sp

eciﬁc role within these pro jects, i.e., the role of a sensor might b e analysingwater in a pro ject fo cused on monitoring the amount of some substances in thewater of a river.

Fig. 1. Some of the main concepts regarding sensor network top ology and devices. SSN isused as a namespace for the SSN-XG ontology; DUL for the DOLCE+DnS Ultralite; EventFfor the Event-Mo del-F; CC for our own Contextualised-Cognitive ontology

Thanks to the ab ove concepts, whenever it b ecomes necessary to automaticallyunderstand the kind of data collected by a sensor, we b elieve that it would b e possible to query the LOD cloud by searching for sensor data that is alreadyA Contextualised Cognitive Persp ective for Linked Sensor Data 5

Fig. 2. The main concepts regarding deﬁnition of sensor role, events and ma jor sensor project which the sensor is involved in. Same namespaces are used as in Fig. 1.

topic-tagged and similar to ours with resp ect to not only the raw sensor datafeatures,i.e., time-stamp intervals, real quantities intervals, etc., but also in repsectto the sensor pro jects topics. For example, the probability of the two sensor datasets b elonging to the same application domain could also b e increased ordecreased according to how often that application domain is related to thatparticular sensor typ e, i.e., water analyser), while it obviously has to b e justiﬁedby exp eriments, that we will conduct in the future.

3 Conclusions and Future Work

In this pap er, we prop osed some means to emulate and improve humanawareness ab out the environment, through emulation of the human cognitive process in sensors. We b elieve that considering the LOD cloud as a representative ofhuman memory and Linked Data linkage as a representative of the asso ciativenature of human minds, we can improve the understanding of reality. As aﬁrststep, we fo cused on the alignment of and some extensions to existing sensorontologies to mo del this cognitive asp ect of sensors.

Future work will be on validating our ontology mo delling choices by experiments. In addition, we plan to build a platform which enables the detection ofsensor context and exp ose it (as well as the sensor data itself ) as Linked Op enData. Finally, we aim at integrating